Autonomous AI Agents
Seven purpose-built agents that continuously discover, classify, document, govern, monitor, build and analyse your enterprise data estate—without manual effort.
Seven Specialised Agents
Discovery Agent
Always-on asset discovery
Continuously crawls every connected data source to find new assets, detect schema changes and surface previously unknown datasets—without human intervention.
Responsibilities
- Crawl connected data sources on configurable schedules
- Detect schema changes and surface drift alerts
- Identify net-new assets and register them in the Context Layer
- Generate initial classification and tagging suggestions
- Maintain connector health and surface connectivity issues
Inputs
Outputs
Classification Agent
Automated sensitivity and domain tagging
Applies PII detection, sensitivity tagging and domain classification to every discovered asset using policy rules and LLM reasoning—at scale, without manual effort.
Responsibilities
- Detect PII and sensitive data patterns across every column
- Apply domain classification using semantic rules and LLM inference
- Tag assets with sensitivity labels (Public, Internal, Confidential, Restricted)
- Route classification exceptions to steward review queues
- Re-classify assets when policy definitions change
Inputs
Outputs
Documentation Agent
Business-friendly descriptions at scale
Generates business-friendly descriptions, usage guidance and example queries for every data asset—eliminating the documentation debt that plagues every enterprise.
Responsibilities
- Generate plain-language asset descriptions from schema and sample data
- Create usage guidance tailored to each data persona
- Suggest example queries and access patterns
- Enrich assets with domain context from the knowledge graph
- Flag assets with insufficient documentation for steward review
Inputs
Outputs
Governance Agent
Real-time policy compliance monitoring
Monitors policy compliance, enforces access rules and flags violations in real time—making governance a continuous process rather than a periodic audit.
Responsibilities
- Monitor every data access event against defined access policies
- Detect and alert on policy violations in real time
- Enforce retention, classification and quality policies automatically
- Generate compliance audit trails for GDPR, HIPAA, SOX, CCPA
- Propose remediation steps for flagged violations
Inputs
Outputs
Quality Agent
Continuous data quality monitoring
Profiles datasets, scores quality across five dimensions and monitors for drift—ensuring consumers always know the trustworthiness of every data product they subscribe to.
Responsibilities
- Profile datasets for completeness, accuracy, freshness, uniqueness and validity
- Score quality against defined SLAs and flag breaches
- Monitor for statistical drift against established baselines
- Surface quality trends and improvement recommendations
- Alert data product owners when quality SLAs are at risk
Inputs
Outputs
Build Agent
Automated data product assembly
Assembles, documents and publishes data products from governed source datasets—following defined contracts and SLAs without requiring manual engineering effort for each product.
Responsibilities
- Assemble data products from governed source assets
- Generate product documentation, schemas and contracts
- Validate quality requirements before publishing
- Register products in the Data Product Registry
- Monitor published products against their contracted SLAs
Inputs
Outputs
Impact Agent
Change impact analysis before you act
Analyses the downstream blast radius of any proposed schema or policy change before it is applied—preventing unintended breakages in AI models, dashboards and data products.
Responsibilities
- Traverse the lineage graph to identify all downstream dependants
- Score impact severity by asset type and consumer criticality
- Generate a prioritised impact report for proposed changes
- Alert owners of affected assets and data products
- Suggest mitigation paths for high-risk changes
Inputs
Outputs
Agents That Work Together
Contivra agents share a common context layer, lineage graph and policy engine. A discovery event triggers classification; classification enriches documentation; governance monitors compliance; quality validates SLAs. The result is a continuously operating, self-reinforcing data operations loop.
Agent Orchestration Flow Diagram
Visualization coming soon
Safe, Auditable, Controllable
Policy Boundaries
Every agent operates within policy-defined boundaries. No agent can access data it is not authorised to see.
Audit Logs
Every agent action—discovery, classification, governance check—produces an immutable, queryable audit log.
Human Override
Stewards can review, override or pause any agent action. Humans remain in control; agents handle the scale.
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